從陣列中洗掉多個元素

我有這個資料：

Temperature = [1054.36258, 1092.96819, 1051.45602, 1058.94926, 1083.66115, 1080.66721
, 1046.96163, 1018.42441, 1049.44307, 1068.91551, 1018.44764, 1035.11906
, 1020.44599,  993.275818 1037.41013, 1005.4047,  1025.73646,  975.301913
, 1000.45743, 1033.21562, 1037.31104,  968.730834, 907.474634, 930.465587
, 998.967526, 911.791887, 915.951873, 829.331306, 931.702088, 890.075633
, 830.659093, 878.715978, 866.238768, 897.958014, 940.495055, 841.990924
, 875.391469, 898.393043, 925.048353, 931.445104, 904.151363, 965.550728
, 916.348809, 936.315168, 900.445995, 887.76832,  875.064126, 881.480871
, 878.240278, 862.958271, 893.813659, 883.678318, 923.593998, 915.52458
, 877.919073, 891.754242, 919.274917, 862.223914, 881.275387, 862.33147
, 869.461632, 890.014577, 902.656117, 874.446393, 876.284046, 866.751916
, 854.095049, 844.540741, 870.263794, 866.687327, 818.019291, 821.875267
, 813.385138, 843.198211, 870.558259, 794.039978, 813.497634, 812.217789
, 801.361143, 800.263045, 747.101493, 735.923635, 732.930255, 775.930026
, 783.786631, 775.255742, 774.938671, 704.186773, 747.612911, 729.315237
, 694.021293, 742.723487, 709.890191, 760.674339, 751.491228, 723.875166
, 741.451471, 749.69441,  743.337883, 700.286359, 720.250078, 732.189596
, 693.097572, 782.342462, 711.995854, 684.432159, 761.195087, 746.725427
, 744.614939, 648.985204, 676.023106, 689.141056, 627.855922, 707.298358
, 652.207871, 652.609278, 680.52524,  689.328581, 678.148423, 728.229663
, 691.857497, 743.998987, 696.885527, 733.249599, 722.833678, 734.832942
, 719.049095, 703.573908, 711.15146,  689.345427]

和 ：

time =[datetime.datetime(2015, 11, 7, 18, 14, 24),
 datetime.datetime(2015, 11, 7, 18, 19, 12),
 datetime.datetime(2015, 11, 7, 18, 23, 9),
 datetime.datetime(2015, 11, 7, 18, 26, 38),
 datetime.datetime(2015, 11, 7, 18, 29, 55),
 datetime.datetime(2015, 11, 7, 18, 32, 52),
 datetime.datetime(2015, 11, 7, 18, 35, 36),
 datetime.datetime(2015, 11, 7, 18, 38, 26),
 datetime.datetime(2015, 11, 7, 18, 41, 13),
 datetime.datetime(2015, 11, 7, 18, 44, 16),
 datetime.datetime(2015, 11, 7, 18, 47, 12),
 datetime.datetime(2015, 11, 7, 18, 50, 1),
 datetime.datetime(2015, 11, 7, 18, 53, 2),
 datetime.datetime(2015, 11, 7, 18, 56, 17),
 datetime.datetime(2015, 11, 7, 18, 59, 45),
 datetime.datetime(2015, 11, 7, 19, 3, 14),
 datetime.datetime(2015, 11, 7, 19, 6, 28),
 datetime.datetime(2015, 11, 7, 19, 10, 4),
 datetime.datetime(2015, 11, 7, 19, 13, 46),
 datetime.datetime(2015, 11, 7, 19, 17, 47),
 datetime.datetime(2015, 11, 7, 19, 21, 35),
 datetime.datetime(2015, 11, 7, 19, 25, 15),
 datetime.datetime(2015, 11, 7, 19, 29, 22),
 datetime.datetime(2015, 11, 7, 19, 33, 41),
 datetime.datetime(2015, 11, 7, 19, 38, 38),
 datetime.datetime(2015, 11, 7, 19, 43, 16),
 datetime.datetime(2015, 11, 7, 19, 47, 53),
 datetime.datetime(2015, 11, 7, 19, 53, 21),
 datetime.datetime(2015, 11, 7, 19, 59, 4),
 datetime.datetime(2015, 11, 7, 20, 5, 14),
 datetime.datetime(2015, 11, 7, 20, 11, 6),
 datetime.datetime(2015, 11, 7, 20, 17, 7),
 datetime.datetime(2015, 11, 7, 20, 24, 11),
 datetime.datetime(2015, 11, 7, 20, 31, 5),
 datetime.datetime(2015, 11, 7, 20, 50, 8),
 datetime.datetime(2015, 11, 7, 20, 54, 31),
 datetime.datetime(2015, 11, 7, 20, 59, 28),
 datetime.datetime(2015, 11, 7, 21, 4, 54),
 datetime.datetime(2015, 11, 7, 21, 10, 24),
 datetime.datetime(2015, 11, 7, 21, 15, 56),
 datetime.datetime(2015, 11, 7, 21, 21, 50),
 datetime.datetime(2015, 11, 7, 21, 33, 24),
 datetime.datetime(2015, 11, 7, 21, 37, 54),
 datetime.datetime(2015, 11, 7, 21, 42, 24),
 datetime.datetime(2015, 11, 7, 21, 47, 20),
 datetime.datetime(2015, 11, 7, 21, 52, 12),
 datetime.datetime(2015, 11, 7, 21, 57, 3),
 datetime.datetime(2015, 11, 7, 22, 1, 41),
 datetime.datetime(2015, 11, 7, 22, 6, 21),
 datetime.datetime(2015, 11, 7, 22, 11, 30),
 datetime.datetime(2015, 11, 7, 22, 16, 44),
 datetime.datetime(2015, 11, 7, 22, 21, 59),
 datetime.datetime(2015, 11, 7, 22, 26, 56),
 datetime.datetime(2015, 11, 7, 22, 32),
 datetime.datetime(2015, 11, 7, 22, 37, 43),
 datetime.datetime(2015, 11, 7, 22, 43, 21),
 datetime.datetime(2015, 11, 7, 22, 48, 45),
 datetime.datetime(2015, 11, 7, 22, 53, 49),
 datetime.datetime(2015, 11, 7, 22, 58, 49),
 datetime.datetime(2015, 11, 7, 23, 4, 4),
 datetime.datetime(2015, 11, 7, 23, 9, 8),
 datetime.datetime(2015, 11, 7, 23, 14, 3),
 datetime.datetime(2015, 11, 7, 23, 18, 34),
 datetime.datetime(2015, 11, 7, 23, 22, 58),
 datetime.datetime(2015, 11, 7, 23, 27, 43),
 datetime.datetime(2015, 11, 7, 23, 32, 22),
 datetime.datetime(2015, 11, 7, 23, 36, 48),
 datetime.datetime(2015, 11, 7, 23, 41, 9),
 datetime.datetime(2015, 11, 7, 23, 45, 29),
 datetime.datetime(2015, 11, 7, 23, 49, 59),
 datetime.datetime(2015, 11, 7, 23, 54, 34),
 datetime.datetime(2015, 11, 7, 23, 59, 6),
 datetime.datetime(2015, 11, 8, 0, 3, 37),
 datetime.datetime(2015, 11, 8, 0, 8, 17),
 datetime.datetime(2015, 11, 8, 0, 13, 15),
 datetime.datetime(2015, 11, 8, 0, 18, 22),
 datetime.datetime(2015, 11, 8, 0, 23, 23),
 datetime.datetime(2015, 11, 8, 0, 28, 28),
 datetime.datetime(2015, 11, 8, 0, 33, 59),
 datetime.datetime(2015, 11, 8, 0, 39, 51),
 datetime.datetime(2015, 11, 8, 0, 45, 56),
 datetime.datetime(2015, 11, 8, 0, 51, 57),
 datetime.datetime(2015, 11, 8, 0, 57, 48),
 datetime.datetime(2015, 11, 8, 1, 4, 2),
 datetime.datetime(2015, 11, 8, 1, 10, 47),
 datetime.datetime(2015, 11, 8, 1, 17, 43),
 datetime.datetime(2015, 11, 8, 1, 24, 22),
 datetime.datetime(2015, 11, 8, 1, 30, 39),
 datetime.datetime(2015, 11, 8, 1, 37, 2),
 datetime.datetime(2015, 11, 8, 1, 43, 51),
 datetime.datetime(2015, 11, 8, 1, 50, 38),
 datetime.datetime(2015, 11, 8, 1, 57, 23),
 datetime.datetime(2015, 11, 8, 2, 4, 3),
 datetime.datetime(2015, 11, 8, 2, 10, 46),
 datetime.datetime(2015, 11, 8, 2, 18, 6),
 datetime.datetime(2015, 11, 8, 2, 25, 14),
 datetime.datetime(2015, 11, 8, 2, 32, 30),
 datetime.datetime(2015, 11, 8, 2, 39, 35),
 datetime.datetime(2015, 11, 8, 2, 46, 49),
 datetime.datetime(2015, 11, 8, 2, 54, 43),
 datetime.datetime(2015, 11, 8, 3, 2, 33),
 datetime.datetime(2015, 11, 8, 3, 10, 15),
 datetime.datetime(2015, 11, 8, 3, 17, 28),
 datetime.datetime(2015, 11, 8, 3, 24, 30),
 datetime.datetime(2015, 11, 8, 3, 32, 8),
 datetime.datetime(2015, 11, 8, 3, 39, 13),
 datetime.datetime(2015, 11, 8, 3, 46, 10),
 datetime.datetime(2015, 11, 8, 3, 52, 48),
 datetime.datetime(2015, 11, 8, 3, 59, 1),
 datetime.datetime(2015, 11, 8, 4, 5, 39),
 datetime.datetime(2015, 11, 8, 4, 11, 59),
 datetime.datetime(2015, 11, 8, 4, 18, 27),
 datetime.datetime(2015, 11, 8, 4, 24, 49),
 datetime.datetime(2015, 11, 8, 4, 31, 7),
 datetime.datetime(2015, 11, 8, 4, 39, 18),
 datetime.datetime(2015, 11, 8, 4, 46, 26),
 datetime.datetime(2015, 11, 8, 4, 53, 13),
 datetime.datetime(2015, 11, 8, 5, 0, 11),
 datetime.datetime(2015, 11, 8, 5, 8, 57),
 datetime.datetime(2015, 11, 8, 5, 15, 45),
 datetime.datetime(2015, 11, 8, 5, 22, 6),
 datetime.datetime(2015, 11, 8, 5, 28, 5),
 datetime.datetime(2015, 11, 8, 5, 34, 57),
 datetime.datetime(2015, 11, 8, 5, 40, 4),
 datetime.datetime(2015, 11, 8, 5, 44, 45),
 datetime.datetime(2015, 11, 8, 5, 49, 8),
 datetime.datetime(2015, 11, 8, 5, 54, 41),
 datetime.datetime(2015, 11, 8, 5, 58, 46),
 datetime.datetime(2015, 11, 8, 6, 2, 35),
 datetime.datetime(2015, 11, 8, 6, 6, 18)]

像這樣繪制：

我的目標是洗掉下面紅色區域中索引 27 和 90 之間的元素：

通過使用此代碼：

time_2 = np.delete(time, slice(27, 90 1))
Temperature_2 = np.delete(Temperature, slice(27, 90 1))

但是通過繪圖，我發現了一個這樣的數字：

我想洗掉下面的紅色區域？

我想洗掉這個紅色區域，以便能夠找到合適的擬合函式。

uj5u.com熱心網友回復：

您可以將不需要的范圍的顏色設定為white：

import matplotlib.pyplot as plt
plt.plot(time[:27], Temperature[:27], 'g--')
plt.plot(time[91:], Temperature[91:], 'g--')
plt.plot(time[27:91], Temperature[27:91], 'w');

輸出：

uj5u.com熱心網友回復：

評論中給出的回應“只需在同一圖中繪制資料集兩次，一次使用左側選擇，一次使用右側選擇。 – 9769953”

是正確的。兩個獨立的情節會很好地作業。話雖如此，這一切都取決于您對洗掉的真正含義。我相信 num py 正在做的是試圖從每個點到下一個點畫一條線。因此，當您洗掉一堆條目時，numpy 仍在嘗試連接點，并且只會從最后一個點到新點的開頭畫一條線。它純粹是視覺效果，您應該能夠在洗掉舊資料的地方添加資料，并且會如您所愿。

uj5u.com熱心網友回復：

您已經弄清楚如何從陣列中洗掉元素，如果您需要進行數值擬合，這就是您想要的。

無論您的資料如何，都會plt.plot繪制連接的線段。假設您有一個更簡單的資料集：

t = np.arange(5)
y = np.array([0, 1, 0, 1, 0])

資料顯示在下面最左邊的圖中。

假設您想讓中間點及其兩個相鄰的線段消失。有幾種解決方案。

第一個是只繪制你關心的部分。Matplotlib 只會將每次呼叫中的點連接到plot：

plt.plot(t[:2], y[:2])
plt.plot(t[3:], y[3:])

此選項的圖表顯示在下面三個圖的中間。請注意，除非您手動覆寫設定，否則顏色回圈儀將為每個段選擇不同的顏色。

一種不同的方法是在資料中插入 NaN，它告訴 matplotlib 不要連接到缺失的點：

ynan = y.astype(float)
ynan[3] = np.nan
plt.plot(t, ynan)

該圖表顯示在右下方。

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